ENERGY STORAGE SYSTEMS USING RENEWABLE ENERGY FOR SYSTEMS WITH GRID INTEGRATION

Abstract

- Freestanding hybrid renewable resources, which mix energy sources like solar, wind, etc., have been found to be an effective replacement for delivering electricity to remote locations that are not connected to utility networks. There are a number of limitations on the adequate supply of electricity, including fluctuations in peak load, the supply of power interruptions, and additional factors such swings. To address these issues, this study offers a practical method for controlling and sizing hybrid renewable energy supplies. First, to predict the uncertainties in load, a Long Short-Term Memory (LSTM) network is used to predict the weather and the loading requirement. This is done to help with resource sizing that is appropriate. Additionally, utilizing the forecasted data, the Shuffled Shepherd Optimization Algorithm is employed to scale resources as effectively as possible. Utilizing MATLAB R2020a, the proposed model is tested by simulating it and comparing its performance to that of other models using metrics like variation rate, battery energy, LPSP, forecasting error, net present cost, and cost of energy.